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Data Mining for Business Analytics & Data Analysis in Python

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Data Mining for Business Analytics & Data Analysis in Python, available at $79.99, has an average rating of 4.41, with 141 lectures, based on 295 reviews, and has 3319 subscribers.

You will learn about Identify the value of data mining for quickly analyzing and interpreting data. Apply data mining algorithms using Python programming language for Business Analytics. Explain the principles behind various data mining algorithms, including supervised and unsupervised machine learning, and explainable AI Explain the results of data mining models using explainable artificial intelligence models: LIME and SHAP. Practice applying data mining techniques through hands-on exercises and case studies. Implement cluster analysis, dimension reduction, and association rule learning using Python. Perform survival analysis, Cox proportional hazard regression, and CHAID using Python. Use random forest and feature selection to improve the accuracy of data mining models. Develop a portfolio of data mining projects for Business Data Analytics and Intelligence. Use data mining techniques to inform business decisions and strategies. This course is ideal for individuals who are Professionals looking to learn Data Mining algorithms or Data Analysts starting to learn Data Mining techniques or Business Analysts looking to learn algorithms on how to uncover business insights or Any Python programmer who would like to learn Data Mining tools It is particularly useful for Professionals looking to learn Data Mining algorithms or Data Analysts starting to learn Data Mining techniques or Business Analysts looking to learn algorithms on how to uncover business insights or Any Python programmer who would like to learn Data Mining tools.

Enroll now: Data Mining for Business Analytics & Data Analysis in Python

Summary

Title: Data Mining for Business Analytics & Data Analysis in Python

Price: $79.99

Average Rating: 4.41

Number of Lectures: 141

Number of Published Lectures: 140

Number of Curriculum Items: 141

Number of Published Curriculum Objects: 140

Original Price: €219.99

Quality Status: approved

Status: Live

What You Will Learn

  • Identify the value of data mining for quickly analyzing and interpreting data.
  • Apply data mining algorithms using Python programming language for Business Analytics.
  • Explain the principles behind various data mining algorithms, including supervised and unsupervised machine learning, and explainable AI
  • Explain the results of data mining models using explainable artificial intelligence models: LIME and SHAP.
  • Practice applying data mining techniques through hands-on exercises and case studies.
  • Implement cluster analysis, dimension reduction, and association rule learning using Python.
  • Perform survival analysis, Cox proportional hazard regression, and CHAID using Python.
  • Use random forest and feature selection to improve the accuracy of data mining models.
  • Develop a portfolio of data mining projects for Business Data Analytics and Intelligence.
  • Use data mining techniques to inform business decisions and strategies.

Who Should Attend

  • Professionals looking to learn Data Mining algorithms
  • Data Analysts starting to learn Data Mining techniques
  • Business Analysts looking to learn algorithms on how to uncover business insights
  • Any Python programmer who would like to learn Data Mining tools

Target Audiences

  • Professionals looking to learn Data Mining algorithms
  • Data Analysts starting to learn Data Mining techniques
  • Business Analysts looking to learn algorithms on how to uncover business insights
  • Any Python programmer who would like to learn Data Mining tools

Are you looking to learn how to do Data Mining like a pro? Do you want to find actionable business insightsusing data science and analytics and explainable artificial intelligence? You have come to the right place.

I will show you the most impactful Data Mining algorithms using Python that I have witnessed in my professional career to derive meaningful insights and interpret data.

In the age of endless spreadsheets, it is easy to feel overwhelmed with so much data. This is where Data Mining techniques come in. To swiftly analyze, find patterns, and deliver an outcome to you. For me, the Data Mining value added is that you stop the number crunching and pivot table creation, leaving time to come with actionable plans based on the insights.

Now, why should you enroll in the course? Let me give you four reasons.

The first is that you will learn the models’ intuition without focusing too much on the math. It is crucial that you know why a model makes sense and the underlying assumptions behind it. I will explain to you each model using words, graphs, and metaphors, leaving math and the Greek alphabet to the bare minimum.

The second reason is the thorough course structure of the most impactful Data Mining techniques for Data Science and Business Analytics.Based on my experience, the course curriculum has the algorithms I believe to be most impactful, up-to-date, and sought after. Here is the list of the algorithms we will learn:

Supervised Machine Learning

  • Survival Analysis

  • Cox Proportional Hazard Regression

  • CHAID

Unsupervised Machine Learning

  • Cluster Analysis – Gaussian Mixture Model

  • Dimension Reduction – PCA and Manifold Learning

  • Association Rule Learning

· Explainable Artificial Intelligence

  • Random Forest and Feature Seletion and Importance

  • LIME

  • XGBoost and SHAP

The third reason is that we code Python together, line by line. Programming is challenging, especially for beginners. I will guide you through every Python code snippet. I will also explain all parameters and functions that you need to use, step by step. In the end, you will have code templates ready to use in your problems.

The final reason is that you practice, practice, practice.At the end of each section, there is a challenge. The goal is that you apply immediately what you have learned. I give you a dataset and a list of actions you need to take to solve it. I think it is the best way to really cement all the techniques in you. Hence, there will be 2 case studies per technique.

I hope to have spiked your interest, and I am looking forward to seeing you inside!

Course Curriculum

Chapter 1: Introduction

Lecture 1: Introduction to Data Mining course for Business Analytics & Data Analysis

Lecture 2: Your resources

Lecture 3: Course Resources, Material, and Colab setup – Important!

Lecture 4: How to get more from the course

Lecture 5: Reviews and the future of the course

Chapter 2: Survival Analysis

Lecture 1: Game Plan for Survival Analysis section

Lecture 2: Survival Analyisis Introduction

Lecture 3: Case Study Briefing and Step by Step Guide

Lecture 4: Python – Changing Directory

Lecture 5: Python – Importing Libraries

Lecture 6: Python – Loading Data

Lecture 7: Python – Transforming Dependent Variable

Lecture 8: Kaplan-Meyer Estimator

Lecture 9: Censoring

Lecture 10: Python – Kaplan-Meyer Estimator

Lecture 11: Python – Calculating Specific Events

Lecture 12: Python – Plotting Survival Curves

Lecture 13: Python – Plotting Cumulative Curves

Lecture 14: Log Rank Test

Lecture 15: Python – Subsetting Dataframe

Lecture 16: Python – Kaplan-Meyer Estimator per Gender

Lecture 17: Python – Plotting both Survival Curves

Lecture 18: Python – Log Rank Test

Lecture 19: Extra Resources and Survival Analysis Challenge

Lecture 20: Python – Survival Analysis Challenge Solutions

Lecture 21: Your feedback is valuable

Chapter 3: Cox Proportional Hazard Regression

Lecture 1: Game Plan

Lecture 2: Cox Proportional Hazard Regression

Lecture 3: Case Study Briefing and Step by Step Guide

Lecture 4: Python – Preparing Script and Data

Lecture 5: Python – Cox Proportional Hazard

Lecture 6: Python – Regression Summary Visualization

Lecture 7: Extra Resources and Challenge

Lecture 8: Python – Solution Challenges

Chapter 4: CHAID

Lecture 1: Game Plan

Lecture 2: Case Study Briefing and Step by Step Guide

Lecture 3: Problem Statement

Lecture 4: Python – Installing libraries

Lecture 5: Python – Importing Libraries and Data

Lecture 6: Introducing CHAID

Lecture 7: CHAID Statistics and Quirks

Lecture 8: Python – Removing column and unique values check

Lecture 9: Python – Visualizing Jobs Variable

Lecture 10: Python – Transforming Jobs Variable

Lecture 11: Python – Transforming Experience Variable

Lecture 12: Python – Transform Minimum Variable

Lecture 13: Python – Modify other variables to dummy variables

Lecture 14: Python – CHAID Preparation

Lecture 15: Python – CHAID Model

Lecture 16: Python – Data Visualization with CHAID Model

Lecture 17: Extra Resources and Challenge

Lecture 18: Python – Challenge solutions

Chapter 5: Cluster Analysis – Gaussian Mixture Model

Lecture 1: Game Plan

Lecture 2: Case Study Briefing and Clustering

Lecture 3: Gaussian Mixture Model vs. Kmeans

Lecture 4: Python – Changing Directory and Importing Libraries

Lecture 5: Python – Loading Data

Lecture 6: AIC, BIC, and Step-by-Step Guide

Lecture 7: Python – Optimal Clusters

Lecture 8: Python – Gaussian Mixture Model

Lecture 9: Python – Cluster Prediction

Lecture 10: Python – Probability of belonging to each cluster

Lecture 11: Python – Cluster Interpretation

Lecture 12: Extra Resources and Challenge

Lecture 13: Python – Challenge solutions

Chapter 6: Dimension Reduction

Lecture 1: Game Plan

Lecture 2: What is Dimension Reduction?

Lecture 3: Principal Component Analysis

Lecture 4: Python – Importing Libraries

Lecture 5: Python – Loading Data

Lecture 6: Python – Transforming String Variables

Lecture 7: Python – Correlation Matrix

Lecture 8: Python – Standardizing Variables

Lecture 9: Python – Optimal Number of Components

Lecture 10: Python – Cumulative Explained Variance

Lecture 11: Python – PCA

Lecture 12: Python – PCA interpretation

Lecture 13: Manifold Learning and t-SNE

Lecture 14: Python – t-SNE

Lecture 15: Python -Visualizing Manifold Learning

Lecture 16: Extra Resources and Challenge

Lecture 17: Python – Challenge Solutions

Chapter 7: Association Rule Learning

Lecture 1: Game Plan

Lecture 2: Step by Step Guide and Case Study Briefing

Lecture 3: Python – Importing Libraries

Lecture 4: Python – Loading Data

Lecture 5: Association Rule Learning

Lecture 6: Python – Create Transaction List

Lecture 7: Python – Encoding Transactions

Lecture 8: Apriori algorithm

Lecture 9: Python – Association Rule Learning

Lecture 10: Python – Apriori Visualization

Lecture 11: Extra Resources and Challenge

Instructors

Rating Distribution

  • 1 stars: 4 votes
  • 2 stars: 2 votes
  • 3 stars: 25 votes
  • 4 stars: 77 votes
  • 5 stars: 188 votes

Frequently Asked Questions


How long do I have access to the course materials?


You can view and review the lecture materials indefinitely, like an on-demand channel.


Can I take my courses with me wherever I go?


Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!

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